MEF-TransUNet: A Newly Developed Remote Sensing Detection Model for Micro Water Body Targets

Micro water bodies are essential to regional ecosystems but are difficult to extract from high-resolution remote sensing images due to fragmentation and building shadows. To address edge breakage and high false-alarm rates in existing semantic segmentation models, this study proposes MEF-TransUNet, an improved TransUNet-based model for fine micro water body extraction. The model integrates a multi-scale edge-guided attention module (MEGA), a high–low-frequency decomposition fusion module (HLFD), and a convolutional block attention module (CBAM). Specifically, MEGA extracts edge priors using a Laplacian pyramid to repair topological breaks in slender water bodies. HLFD uses frequency-domain decoupling to suppress high-frequency background noise and reduce confusion between water bodies and shadows. CBAM enhances channel and spatial feature attention. Experiments using PlanetScope images from the Songhuajiang River Basin in Daqing City of the Heilongjiang Province in China showed that MEF-TransUNet achieves 91.74% precision, a 90.07% F1-score, a recall of 90.22%, and a B-IoU of 43.88%. For the GID dataset, the model attains a precision of 91.85%, an F1-score of 91.48%, a recall of 92.01%, and a B-IoU of 55.42%. Its overall performance clearly outperforms DeepLabV3+, SegFormer, U-Net, AttenUNet, and UNet++, enabling accurate micro water body localization, high output purity, and reduced manual correction costs, thus supporting fine water resource management in complex surface environments.